Principal Machine Learning Engineer

Comcast Comcast · Media · Philadelphia, PA

Principal Machine Learning Engineer to lead the design and evolution of large-scale AI platforms powering search, ranking, and recommendations used by millions of customers. This role involves shaping technical strategy, building production-grade ML systems, and driving innovation in personalization, generative AI, and real-time decisioning, working at the intersection of applied research and engineering.

What you'd actually do

  1. Own the architecture, roadmap, and evolution of core ML platforms supporting search, ranking, and recommendation systems
  2. Define technical direction and deliver scalable solutions that enable personalization and relevance at massive scale
  3. Lead complex, revenue-driving initiatives where machine learning is a key differentiator
  4. Advance modern AI approaches including: Recommender systems and learning-to-rank models, Generative AI, LLM fine-tuning, and prompt engineering, Agentic AI systems integrating models with tools and workflows
  5. Design and deploy distributed ML systems and real-time inference pipelines

Skills

Required

  • 10+ years of experience in machine learning, AI, or software engineering
  • Proven track record building and scaling production ML systems
  • Strong Python programming skills with a focus on performance and reliability
  • Experience translating advanced models or research into real-world products
  • Background in search, ranking, recommendations, or personalization systems
  • Familiarity with LLMs, generative AI, or agent-based systems

Nice to have

  • Business Acumen
  • Communication
  • Design
  • Learning Agility
  • Machine Learning (ML)
  • Technical Leadership

What the JD emphasized

  • lead the design and evolution of large-scale AI platforms
  • build production-grade ML systems
  • drive innovation
  • scalable solutions
  • massive scale
  • revenue-driving initiatives
  • modern AI approaches
  • Agentic AI systems
  • leading edge of AI
  • production systems
  • distributed ML systems
  • real-time inference pipelines
  • end-to-end machine learning solutions
  • model deployment, monitoring, and optimization
  • production-quality code
  • scale innovation

Other signals

  • leading the design and evolution of large-scale AI platforms
  • building production-grade ML systems
  • driving innovation in personalization, generative AI, and real-time decisioning
  • architecting and scaling ML systems
  • designing and deploying distributed ML systems and real-time inference pipelines